Bag of Features (BoF) Based Deep Learning Framework for Bleached Corals Detection
Coral reefs are the sub-aqueous calcium carbonate structures collected by the invertebrates known as corals. The charm and beauty of coral reefs attract tourists, and they play a vital role in preserving biodiversity, ceasing coastal erosion, and promoting business trade. However, they are declining...
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ftdoajarticles:oai:doaj.org/article:c172a7fac676410eb9009bfceac9c601 2023-05-15T17:51:59+02:00 Bag of Features (BoF) Based Deep Learning Framework for Bleached Corals Detection Sonain Jamil MuhibUr Rahman Amir Haider 2021-10-01T00:00:00Z https://doi.org/10.3390/bdcc5040053 https://doaj.org/article/c172a7fac676410eb9009bfceac9c601 EN eng MDPI AG https://www.mdpi.com/2504-2289/5/4/53 https://doaj.org/toc/2504-2289 doi:10.3390/bdcc5040053 2504-2289 https://doaj.org/article/c172a7fac676410eb9009bfceac9c601 Big Data and Cognitive Computing, Vol 5, Iss 53, p 53 (2021) CoralNet support vector machine (SVM) bleached corals feature extraction coastal erosion marine safety Technology T article 2021 ftdoajarticles https://doi.org/10.3390/bdcc5040053 2022-12-31T09:44:46Z Coral reefs are the sub-aqueous calcium carbonate structures collected by the invertebrates known as corals. The charm and beauty of coral reefs attract tourists, and they play a vital role in preserving biodiversity, ceasing coastal erosion, and promoting business trade. However, they are declining because of over-exploitation, damaging fishery, marine pollution, and global climate changes. Also, coral reefs help treat human immune-deficiency virus (HIV), heart disease, and coastal erosion. The corals of Australia’s great barrier reef have started bleaching due to the ocean acidification, and global warming, which is an alarming threat to the earth’s ecosystem. Many techniques have been developed to address such issues. However, each method has a limitation due to the low resolution of images, diverse weather conditions, etc. In this paper, we propose a bag of features (BoF) based approach that can detect and localize the bleached corals before the safety measures are applied. The dataset contains images of bleached and unbleached corals, and various kernels are used to support the vector machine so that extracted features can be classified. The accuracy of handcrafted descriptors and deep convolutional neural networks is analyzed and provided in detail with comparison to the current method. Various handcrafted descriptors like local binary pattern, a histogram of an oriented gradient, locally encoded transform feature histogram, gray level co-occurrence matrix, and completed joint scale local binary pattern are used for feature extraction. Specific deep convolutional neural networks such as AlexNet, GoogLeNet, VGG-19, ResNet-50, Inception v3, and CoralNet are being used for feature extraction. From experimental analysis and results, the proposed technique outperforms in comparison to the current state-of-the-art methods. The proposed technique achieves 99.08% accuracy with a classification error of 0.92%. A novel bleached coral positioning algorithm is also proposed to locate bleached corals in the coral reef ... Article in Journal/Newspaper Ocean acidification Directory of Open Access Journals: DOAJ Articles Big Data and Cognitive Computing 5 4 53 |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
CoralNet support vector machine (SVM) bleached corals feature extraction coastal erosion marine safety Technology T |
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CoralNet support vector machine (SVM) bleached corals feature extraction coastal erosion marine safety Technology T Sonain Jamil MuhibUr Rahman Amir Haider Bag of Features (BoF) Based Deep Learning Framework for Bleached Corals Detection |
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CoralNet support vector machine (SVM) bleached corals feature extraction coastal erosion marine safety Technology T |
description |
Coral reefs are the sub-aqueous calcium carbonate structures collected by the invertebrates known as corals. The charm and beauty of coral reefs attract tourists, and they play a vital role in preserving biodiversity, ceasing coastal erosion, and promoting business trade. However, they are declining because of over-exploitation, damaging fishery, marine pollution, and global climate changes. Also, coral reefs help treat human immune-deficiency virus (HIV), heart disease, and coastal erosion. The corals of Australia’s great barrier reef have started bleaching due to the ocean acidification, and global warming, which is an alarming threat to the earth’s ecosystem. Many techniques have been developed to address such issues. However, each method has a limitation due to the low resolution of images, diverse weather conditions, etc. In this paper, we propose a bag of features (BoF) based approach that can detect and localize the bleached corals before the safety measures are applied. The dataset contains images of bleached and unbleached corals, and various kernels are used to support the vector machine so that extracted features can be classified. The accuracy of handcrafted descriptors and deep convolutional neural networks is analyzed and provided in detail with comparison to the current method. Various handcrafted descriptors like local binary pattern, a histogram of an oriented gradient, locally encoded transform feature histogram, gray level co-occurrence matrix, and completed joint scale local binary pattern are used for feature extraction. Specific deep convolutional neural networks such as AlexNet, GoogLeNet, VGG-19, ResNet-50, Inception v3, and CoralNet are being used for feature extraction. From experimental analysis and results, the proposed technique outperforms in comparison to the current state-of-the-art methods. The proposed technique achieves 99.08% accuracy with a classification error of 0.92%. A novel bleached coral positioning algorithm is also proposed to locate bleached corals in the coral reef ... |
format |
Article in Journal/Newspaper |
author |
Sonain Jamil MuhibUr Rahman Amir Haider |
author_facet |
Sonain Jamil MuhibUr Rahman Amir Haider |
author_sort |
Sonain Jamil |
title |
Bag of Features (BoF) Based Deep Learning Framework for Bleached Corals Detection |
title_short |
Bag of Features (BoF) Based Deep Learning Framework for Bleached Corals Detection |
title_full |
Bag of Features (BoF) Based Deep Learning Framework for Bleached Corals Detection |
title_fullStr |
Bag of Features (BoF) Based Deep Learning Framework for Bleached Corals Detection |
title_full_unstemmed |
Bag of Features (BoF) Based Deep Learning Framework for Bleached Corals Detection |
title_sort |
bag of features (bof) based deep learning framework for bleached corals detection |
publisher |
MDPI AG |
publishDate |
2021 |
url |
https://doi.org/10.3390/bdcc5040053 https://doaj.org/article/c172a7fac676410eb9009bfceac9c601 |
genre |
Ocean acidification |
genre_facet |
Ocean acidification |
op_source |
Big Data and Cognitive Computing, Vol 5, Iss 53, p 53 (2021) |
op_relation |
https://www.mdpi.com/2504-2289/5/4/53 https://doaj.org/toc/2504-2289 doi:10.3390/bdcc5040053 2504-2289 https://doaj.org/article/c172a7fac676410eb9009bfceac9c601 |
op_doi |
https://doi.org/10.3390/bdcc5040053 |
container_title |
Big Data and Cognitive Computing |
container_volume |
5 |
container_issue |
4 |
container_start_page |
53 |
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1766159298411364352 |